





'''simple docstring'''






import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCAmelCase									=   """platform"""
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 		lowerCamelCase	:
	snake_case_   = PegasusConfig
	snake_case_   = {}
	snake_case_   = "gelu"



	def __init__( self		,   a_		,   a_=13		,   a_=7		,   a_=True		,   a_=False		,   a_=99		,   a_=32		,   a_=5		,   a_=4		,   a_=37		,   a_=0.1		,   a_=0.1		,   a_=20		,   a_=2		,   a_=1		,   a_=0		,   ):
			lowerCAmelCase  :     Optional[Any]       						= parent
			lowerCAmelCase  :     List[str]       						= batch_size
			lowerCAmelCase  :     Optional[Any]       						= seq_length
			lowerCAmelCase  :     Union[str, Any]       						= is_training
			lowerCAmelCase  :     Dict       						= use_labels
			lowerCAmelCase  :     List[str]       						= vocab_size
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     List[str]       						= num_hidden_layers
			lowerCAmelCase  :     Optional[Any]       						= num_attention_heads
			lowerCAmelCase  :     Optional[Any]       						= intermediate_size

			lowerCAmelCase  :     Tuple       						= hidden_dropout_prob
			lowerCAmelCase  :     Dict       						= attention_probs_dropout_prob
			lowerCAmelCase  :     Union[str, Any]       						= max_position_embeddings
			lowerCAmelCase  :     List[str]       						= eos_token_id
			lowerCAmelCase  :     List[Any]       						= pad_token_id
			lowerCAmelCase  :     int       						= bos_token_id



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size						).clip(3		,   self.vocab_size						)
			lowerCAmelCase  :     List[Any]       						= np.expand_dims(np.array([self.eos_token_id] * self.batch_size						)		,   1						)
			lowerCAmelCase  :     Tuple       						= np.concatenate([input_ids, eos_tensor]		,   axis=1						)

			lowerCAmelCase  :     List[str]       						= ids_tensor([self.batch_size, self.seq_length]		,   self.vocab_size						)

			lowerCAmelCase  :     Optional[int]       						= self.config_cls(
			    vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   encoder_layers=self.num_hidden_layers		,   decoder_layers=self.num_hidden_layers		,   encoder_attention_heads=self.num_attention_heads		,   decoder_attention_heads=self.num_attention_heads		,   encoder_ffn_dim=self.intermediate_size		,   decoder_ffn_dim=self.intermediate_size		,   dropout=self.hidden_dropout_prob		,   attention_dropout=self.attention_probs_dropout_prob		,   max_position_embeddings=self.max_position_embeddings		,   eos_token_ids=[2]		,   bos_token_id=self.bos_token_id		,   pad_token_id=self.pad_token_id		,   decoder_start_token_id=self.pad_token_id		,   **self.config_updates		,   )
			lowerCAmelCase  :     Dict       						= prepare_pegasus_inputs_dict(a_		,   a_		,   a_						)
			return config, inputs_dict



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Any       						= 20
			lowerCAmelCase  :     Dict       						= model_class_name(a_						)

			lowerCAmelCase  :     Union[str, Any]       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     Any       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     List[Any]       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype="i4"						)

			lowerCAmelCase  :     Dict       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )
			lowerCAmelCase  :     Tuple       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     Any       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Union[str, Any]       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     List[Any]       						= model.decode(a_		,   a_						)

			lowerCAmelCase  :     Optional[int]       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     str       						= 20
			lowerCAmelCase  :     Optional[int]       						= model_class_name(a_						)

			lowerCAmelCase  :     Tuple       						= model.encode(inputs_dict["input_ids"]						)

			lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= (
			    inputs_dict["decoder_input_ids"],
			    inputs_dict["decoder_attention_mask"],
			)

			lowerCAmelCase  :     List[Any]       						= jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
			    ]		,   axis=-1		,   )

			lowerCAmelCase  :     Tuple       						= model.init_cache(decoder_input_ids.shape[0]		,   a_		,   a_						)
			lowerCAmelCase  :     Any       						= jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )

			lowerCAmelCase  :     Dict       						= model.decode(
			    decoder_input_ids[:, :-1]		,   a_		,   decoder_attention_mask=a_		,   past_key_values=a_		,   decoder_position_ids=a_		,   )
			lowerCAmelCase  :     Union[str, Any]       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype="i4"						)
			lowerCAmelCase  :     Optional[int]       						= model.decode(
			    decoder_input_ids[:, -1:]		,   a_		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=a_		,   decoder_position_ids=a_		,   )

			lowerCAmelCase  :     Optional[int]       						= model.decode(a_		,   a_		,   decoder_attention_mask=a_						)

			lowerCAmelCase  :     List[Any]       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
			self.parent.assertTrue(diff < 1e-3		,   msg=F'''Max diff is {diff}'''						)






def   __A    (				a_    :	str   ,a_    :	Any   ,a_    :	Tuple   ,a_    :	Any=None   ,a_    :	str=None   ,):
		if attention_mask is None:
				lowerCAmelCase  :     Optional[Any]       						= np.not_equal(a_   ,config.pad_token_id						).astype(np.inta						)
		if decoder_attention_mask is None:
				lowerCAmelCase  :     Any       						= np.concatenate(
				    [
				        np.ones(decoder_input_ids[:, :1].shape   ,dtype=np.inta						),
				        np.not_equal(decoder_input_ids[:, 1:]   ,config.pad_token_id						).astype(np.inta						),
				    ]   ,axis=-1   ,)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		}

@require_flax
class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = (
	    (
	        FlaxPegasusForConditionalGeneration,
	        FlaxPegasusModel,
	    )
	    if is_flax_available()
	    else ()
	)
	snake_case_   = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
	snake_case_   = True
	snake_case_   = False
	snake_case_   = False
	snake_case_   = False



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= FlaxPegasusModelTester(self						)
			lowerCAmelCase  :     Optional[Any]       						= ConfigTester(self		,   config_class=a_						)



	def 	_lowerCamelCase     ( self						):
			self.config_tester.run_common_tests()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     Dict       						= self._prepare_for_class(a_		,   a_						)
							lowerCAmelCase  :     int       						= model_class(a_						)

							@jax.jit
							def encode_jitted(a_		,   a_=None		,   **a_						):
									return model.encode(input_ids=a_		,   attention_mask=a_						)

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     Dict       						= encode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     str       						= encode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase  :     int       						= model_class(a_						)
							lowerCAmelCase  :     Optional[int]       						= model.encode(inputs_dict["input_ids"]		,   inputs_dict["attention_mask"]						)

							lowerCAmelCase  :     Dict       						= {
							    "decoder_input_ids": inputs_dict["decoder_input_ids"],
							    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
							    "encoder_outputs": encoder_outputs,
							}

							@jax.jit
							def decode_jitted(a_		,   a_		,   a_						):
									return model.decode(
									    decoder_input_ids=a_		,   decoder_attention_mask=a_		,   encoder_outputs=a_		,   )

							with self.subTest("JIT Enabled"						):
									lowerCAmelCase  :     List[str]       						= decode_jitted(**a_						).to_tuple()

							with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
											lowerCAmelCase  :     Union[str, Any]       						= decode_jitted(**a_						).to_tuple()

							self.assertEqual(len(a_						)		,   len(a_						)						)
							for jitted_output, output in zip(a_		,   a_						):
									self.assertEqual(jitted_output.shape		,   output.shape						)



	@slow
	def 	_lowerCamelCase     ( self						):
			for model_class_name in self.all_model_classes:
					lowerCAmelCase  :     List[str]       						= model_class_name.from_pretrained("google/pegasus-large"		,   from_pt=a_						)
					lowerCAmelCase  :     int       						= np.ones((1, 1)						)
					lowerCAmelCase  :     List[str]       						= model(a_						)
					self.assertIsNotNone(a_						)





	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"						)
			lowerCAmelCase  :     int       						= PegasusTokenizer.from_pretrained("google/pegasus-xsum"						)

			lowerCAmelCase  :     str       						= [
			    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
			    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
			]

			lowerCAmelCase  :     Union[str, Any]       						= [
			    "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
			    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
			]

			lowerCAmelCase  :     List[str]       						= tokenizer(a_		,   return_tensors="np"		,   truncation=a_		,   max_length=512		,   padding=a_						)
			lowerCAmelCase  :     Tuple       						= model.generate(**a_		,   num_beams=2						).sequences
			lowerCAmelCase  :     List[str]       						= tokenizer.batch_decode(a_		,   skip_special_tokens=a_						)
			assert tgt_text == decoded










'''simple docstring'''






import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase									=   logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   *a_		,   a_=None		,   a_=None		,   a_=None		,   **a_						):
			super().__init__(*a_		,   **a_						)
			lowerCAmelCase  :     Tuple       						= eval_examples
			lowerCAmelCase  :     List[Any]       						= post_process_function
			lowerCAmelCase  :     List[Any]       						= quant_trainer_args
			lowerCAmelCase  :     Dict       						= 128  # default number of calibration samples



	def 	_lowerCamelCase     ( self		,   a_=None						):
			if calib_dataset is None and self.calib_dataset is None:
					raise ValueError("Trainer: calibration requires an calib_dataset."						)
			lowerCAmelCase  :     List[str]       						= calib_dataset if calib_dataset is not None else self.calib_dataset

			lowerCAmelCase  :     Union[str, Any]       						= self._remove_unused_columns(a_		,   description="Calibration"						)

			return DataLoader(
			    a_		,   batch_size=self.args.eval_batch_size		,   collate_fn=self.data_collator		,   drop_last=self.args.dataloader_drop_last		,   num_workers=self.args.dataloader_num_workers		,   pin_memory=self.args.dataloader_pin_memory		,   shuffle=a_		,   )



	def 	_lowerCamelCase     ( self		,   a_=None						):
			lowerCAmelCase  :     str       						= self.train_dataset if calib_dataset is None else calib_dataset
			lowerCAmelCase  :     str       						= self.get_calib_dataloader(a_						)

			lowerCAmelCase  :     Tuple       						= self.model
			quant_trainer.configure_model(a_		,   self.quant_trainer_args		,   calib=a_						)
			model.eval()
			quant_trainer.enable_calibration(a_						)

			logger.info("***** Running calibration *****"						)
			logger.info(F'''  Num examples = {self.calib_num}'''						)
			logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''						)

			for step, inputs in enumerate(a_						):
					# Prediction step
					lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= self.prediction_step(a_		,   a_		,   prediction_loss_only=a_						)
					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

			quant_trainer.finish_calibration(a_		,   self.quant_trainer_args						)
			lowerCAmelCase  :     Union[str, Any]       						= model



	def 	_lowerCamelCase     ( self		,   a_=None		,   a_=None		,   a_=None		,   a_ = "eval"						):
			lowerCAmelCase  :     Optional[Any]       						= self.eval_dataset if eval_dataset is None else eval_dataset
			lowerCAmelCase  :     Optional[Any]       						= self.get_eval_dataloader(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= self.eval_examples if eval_examples is None else eval_examples

			# Temporarily disable metric computation, we will do it in the loop here.
			lowerCAmelCase  :     Dict       						= self.compute_metrics
			lowerCAmelCase  :     List[str]       						= None
			lowerCAmelCase  :     str       						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					lowerCAmelCase  :     Union[str, Any]       						= eval_loop(
					    a_		,   description="Evaluation"		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=a_		,   )
			finally:
					lowerCAmelCase  :     List[str]       						= compute_metrics

			if self.post_process_function is not None and self.compute_metrics is not None:
					lowerCAmelCase  :     Optional[Any]       						= self.post_process_function(a_		,   a_		,   output.predictions						)
					lowerCAmelCase  :     Optional[Any]       						= self.compute_metrics(a_						)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()						):
							if not key.startswith(F'''{metric_key_prefix}_'''						):
									lowerCAmelCase  :     Any       						= metrics.pop(a_						)

					self.log(a_						)
			else:
					lowerCAmelCase  :     str       						= {}

			if self.args.tpu_metrics_debug or self.args.debug:
					# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
					xm.master_print(met.metrics_report()						)

			lowerCAmelCase  :     Dict       						= self.callback_handler.on_evaluate(self.args		,   self.state		,   self.control		,   a_						)
			return metrics



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_=None		,   a_ = "test"						):
			lowerCAmelCase  :     int       						= self.get_test_dataloader(a_						)

			# Temporarily disable metric computation, we will do it in the loop here.
			lowerCAmelCase  :     Union[str, Any]       						= self.compute_metrics
			lowerCAmelCase  :     List[str]       						= None
			lowerCAmelCase  :     Union[str, Any]       						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					lowerCAmelCase  :     int       						= eval_loop(
					    a_		,   description="Prediction"		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=a_		,   )
			finally:
					lowerCAmelCase  :     str       						= compute_metrics

			if self.post_process_function is None or self.compute_metrics is None:
					return output

			lowerCAmelCase  :     List[str]       						= self.post_process_function(a_		,   a_		,   output.predictions		,   "predict"						)
			lowerCAmelCase  :     Any       						= self.compute_metrics(a_						)

			# Prefix all keys with metric_key_prefix + '_'
			for key in list(metrics.keys()						):
					if not key.startswith(F'''{metric_key_prefix}_'''						):
							lowerCAmelCase  :     Optional[Any]       						= metrics.pop(a_						)

			return PredictionOutput(predictions=predictions.predictions		,   label_ids=predictions.label_ids		,   metrics=a_						)





	def 	_lowerCamelCase     ( self		,   a_="./"						):
			lowerCAmelCase  :     Optional[Any]       						= self.eval_dataset
			lowerCAmelCase  :     Optional[int]       						= self.get_eval_dataloader(a_						)

			lowerCAmelCase  :     Union[str, Any]       						= next(iter(a_						)						)

			# saving device - to make it consistent
			lowerCAmelCase  :     Any       						= torch.device("cuda" if torch.cuda.is_available() else "cpu"						)

			# convert to tuple
			lowerCAmelCase  :     str       						= tuple(v.to(a_						) for k, v in batch.items()						)

			logger.info("Converting model to be onnx compatible"						)
			from pytorch_quantization.nn import TensorQuantizer

			lowerCAmelCase  :     Any       						= True

			lowerCAmelCase  :     Dict       						= self.model.to(a_						)

			model.eval()
			model.float()

			lowerCAmelCase  :     Union[str, Any]       						= model.module if hasattr(a_		,   "module"						) else model
			quant_trainer.configure_model(a_		,   self.quant_trainer_args						)

			lowerCAmelCase  :     Any       						= os.path.join(a_		,   "model.onnx"						)
			logger.info(F'''exporting model to {output_model_file}'''						)

			lowerCAmelCase  :     List[Any]       						= {0: "batch_size", 1: "seq_len"}

			torch.onnx.export(
			    a_		,   a_		,   a_		,   export_params=a_		,   opset_version=13		,   do_constant_folding=a_		,   input_names=["input_ids", "attention_mask", "token_type_ids"]		,   output_names=["output_start_logits", "output_end_logits"]		,   dynamic_axes={
			        "input_ids": axes,
			        "attention_mask": axes,
			        "token_type_ids": axes,
			        "output_start_logits": axes,
			        "output_end_logits": axes,
			    }		,   verbose=a_		,   )
			logger.info("onnx export finished"						)



